Industrial diagnostic systems increasingly combine machine-learning classifiers, temporal models, uncertainty surrogates, and explanation methods. Their operational use requires a separate mechanism that determines whether the available evidence is valid, mutually consistent, sufficient for a recommendation, or should instead lead to abstention or human escalation. This paper introduces the Diagnostic Evidence Governance Agent (DEGA), a deterministic agent that separates evidence generation from workflow governance. DEGA operates on immutable evidence objects, follows an explicit finite-state workflow, applies a higher-priority SafetyGuard, and records a hash-linked audit trace supporting deterministic replay. The architecture is demonstrated in an offline case study using eight DUDU-BLDC acquisitions, 200 non-overlapping windows, 28 current- and speed-based features, three classifiers, and four spline representations. Across 16 dependent acquisition-disjoint assignment views, mean acquisition-level macro-F1 was 0.875 for Logistic Regression and 0.667 for both tree-based classifiers. The pipeline produced 768 complete EvidenceBundles with reproducible scientific hashes. Explanation rankings were assignment-sensitive, with a mean top-10 Jaccard similarity of 0.318. Twelve representative DEGA executions ended in three escalations and nine no-decision outcomes, with complete audit chains and deterministic replay. The study demonstrates auditable, fail-closed governance of AI-supported diagnostic evidence without delegating workflow control to an AI agent.

Industrial diagnostic systems require a governance mechanism that determines whether heterogeneous evidence is admissible, mutually consistent, and sufficient for an automatic action. This paper presents the Diagnostic Evidence Governance Agent (DEGA), a deterministic governance layer built from explicit finite-state machine states, replaceable routing policies, an authoritative SafetyGuard, and hash-linked audit with deterministic replay. The bounded DUDU-BLDC case study retains diagnostic evidence from eight acquisitions and extends the evaluation to governance-profile sensitivity and state coverage. Across 15,360 retained real-evidence case-policy routes, no automatic recommendation was issued; 9216 routes reached Decision Check; and the mandatory-explanation rule triggered 7904 times. Under predeclared contract-satisfied simulated profiles, 2924 recommendations and 12,436 escalations were produced, with no recommended route violating the internal governance-consistency proxy. The results show that explanation availability is a binding governance condition and that recommendation-path reachability can be evaluated without weakening the remaining SafetyGuard gates; the simulation is restricted to explanation availability and admissibility metadata and does not validate feature attribution, recommendation correctness, or industrial deployment.